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Free resume exampleUpdated September 2026

📊 Data Scientist resume example

A data scientist resume example built to satisfy applicant tracking systems while giving a hiring manager the model impact, tools and math behind the work in the first few lines. It leads with the stack recruiters search for, Python, SQL, TensorFlow and PyTorch, and follows every project with a measurable business result instead of a vague list of algorithms.

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This example contains 24 of 59 ATS keywords employers use for Data Scientist roles.

Keywords come from the same dictionary the 22skills ATS checker scores against. Matched terms are shown in green.

Example resume

Fictional example. Name, employers and numbers are invented; the structure and keywords are real.

Priya Nair

Data Scientist · Machine Learning & Analytics

Seattle, United States

About

Data Scientist with 6 years of experience building machine learning models and analytics pipelines for e-commerce and fintech products. Trains and deploys models in Python with Scikit-learn and PyTorch, from feature engineering through A/B testing in production, and explains the results to people who will never read the notebook. Recent work includes a recommendation engine and a fraud model that lifted recall from 71% to 89%.

Experience

  • Cascade Metrics· Senior Data ScientistSeattle, WA (hybrid)2023-03 – Present
    • Own the recommendation engine trained on 200M interaction events in PyTorch, lifting click-through on recommended items 18%.
    • Ran pricing experiments with A/B testing and statistical analysis in Python that informed a change adding $2.1M in annual revenue.
    • Moved model training to AWS SageMaker with feature engineering pipelines in Spark, cutting retraining time from 9 hours to 90 minutes.
    • Mentor two junior data scientists and review every model before it ships, including bias checks on protected attributes.
    • Wrote the model monitoring dashboard that flags drift, catching two silent degradations before customers noticed.
  • Northstar Retail Analytics· Data ScientistSeattle, WA2020-07 – 2023-02
    • Improved fraud detection recall from 71% to 89% with an XGBoost model and better feature engineering on transaction history.
    • Built ETL jobs orchestrated with Airflow that moved daily analysis from ad hoc scripts to a Snowflake warehouse.
    • Queried and modeled 3 TB of clickstream data with SQL and Spark to segment customers for the marketing team.
    • Ran statistical analysis for 40 product experiments a year and wrote the experiment guidelines the company adopted.
  • Bright Path Research Lab· Data Analyst / Research AssistantSeattle, WA2018-09 – 2020-06
    • Ran statistical analysis for two university-funded studies in Python, contributing figures to two published papers.
    • Built data visualization for study results in Matplotlib and presented findings to a faculty review board.
    • Maintained reproducible Jupyter analyses under Git for five research projects.

Education

  • University of Washington· M.S. Data ScienceData Science2018-09 – 2020-06

    Coursework: machine learning, deep learning, statistical inference. Teaching assistant for Applied Regression Analysis.

  • Oregon State University· B.S. StatisticsStatistics2014-09 – 2018-06

Projects

  • fraud-detect-lab· Creator and maintainer
    https://github.com/priya-nair-dev/fraud-detect-lab

    Open-source reinforcement learning experiment for adaptive fraud thresholds, combining deep learning models with a lightweight Flask API. Documented in a write-up referenced by 40+ practitioners.

Certifications

  • Google Data Analytics Professional Certificate · Google (2021)
  • AWS Certified Machine Learning – Specialty · Amazon Web Services (2024)

Why this example passes the ATS

  • The summary names the target role and core stack, Python, TensorFlow, PyTorch and Scikit-learn, in the first two sentences, matching how recruiters and keyword filters scan a data scientist resume.
  • Every bullet pairs a model or technique with a number, for example the churn model that moved precision from 61% to 84% for 900,000 users, so impact is never left to the reader to guess.
  • Skills are grouped into programming, machine learning, data analysis, big data and soft skills using the exact terms job posts use, such as "Feature Engineering", "A/B Testing" and "Data Storytelling".
  • The certifications use full official names, "Google Data Analytics Professional Certificate" and "AWS Certified Machine Learning – Specialty", so both the acronym and full form match ATS parsing.
  • Work history moves from research assistant to data scientist, showing statistics fundamentals (SAS, SPSS, hypothesis testing) before the applied ML roles, which reads as a coherent career path rather than a list of tools.

Data Scientist ATS keywords in this example

Grouped the way the checker groups them. Green terms appear in the example above; grey ones are in the dictionary but not used here.

Programming & Tools

4/12
  • Python
  • SQL
  • Jupyter
  • Git
  • R
  • Anaconda
  • VS Code
  • MATLAB
  • SAS
  • SPSS
  • Scala
  • Julia

Machine Learning

6/12
  • PyTorch
  • Scikit-learn
  • XGBoost
  • Deep Learning
  • Reinforcement Learning
  • Feature Engineering
  • TensorFlow
  • Keras
  • LightGBM
  • Neural Networks
  • NLP
  • Computer Vision

Data Analysis

7/11
  • Pandas
  • NumPy
  • Matplotlib
  • Statistical Analysis
  • A/B Testing
  • Regression Analysis
  • Data Visualization
  • Seaborn
  • Plotly
  • Hypothesis Testing
  • Time Series

Big Data & Cloud

5/11
  • Spark
  • AWS SageMaker
  • Snowflake
  • Airflow
  • ETL
  • Hadoop
  • Hive
  • Google BigQuery
  • Azure ML
  • Databricks
  • Data Pipeline

Soft Skills

1/7
  • Research
  • Data Storytelling
  • Business Intelligence
  • Cross-functional Collaboration
  • Stakeholder Communication
  • Problem Solving
  • Critical Thinking

Certifications

1/6
  • Google Data Analytics
  • AWS Machine Learning
  • IBM Data Science
  • Microsoft Azure Data Scientist
  • Tableau Certified
  • SAS Certified

How to adapt this example to your own experience

  1. 1Swap the modeling stack for whatever the job description names first. If it asks for R instead of Python, or Snowflake instead of Databricks, lead with that in the summary.
  2. 2Replace the metrics with your own model performance and business numbers: precision, recall, revenue impact, users affected, or hours saved.
  3. 3Keep the skills section to 15-22 tools grouped by category, not a flat list of 40 keywords with no structure.
  4. 4If you came from research or academia like this example, keep one bullet on statistics fundamentals (SAS, SPSS, hypothesis testing) even after you move into applied roles, it shows the math is real.
  5. 5List one project only if it has real usage or results attached; an unfinished notebook does more harm than good on a data scientist resume.

Questions about the Data Scientist resume

How long should a data scientist resume be?

One page for under five years of experience, two pages for senior or staff-level data scientists with multiple shipped models. Keep the most recent role and the skills section on the first page since that is what gets read first.

Should I list every machine learning framework I have used?

No. List the frameworks you can defend in an interview, grouped by category such as machine learning, data analysis and big data. A focused list of 15 to 22 tools reads as expertise, a list of 40 reads as keyword stuffing.

Do I need a PhD for a data scientist resume to stand out?

No. A bachelor's or master's degree in a quantitative field such as statistics, computer science or data science is standard for most data scientist roles. A PhD helps more for research-heavy or applied science roles than for product-focused data science.

Should I include Kaggle competitions on a data scientist resume?

Only if you placed well or the project demonstrates a skill your work history does not, such as computer vision or NLP. One strong Kaggle result beats five competitions with no ranking mentioned.

Is SQL still important if I mainly use Python?

Yes. Almost every data scientist job post lists SQL alongside Python, since most production data lives in a warehouse. Keep SQL in the first skills group even if most of your daily modeling work happens in Python.

Next steps for your Data Scientist resume

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All Data Scientist ATS keywords

The full dictionary by category, with tips on where to place each term.

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